AI Value: A 4-Step Guide to Business Impact

Step 1: Ideate – Identify the Value Drivers

The first step toward generating real value with AI is understanding which parts of your business process actually drive that value. In other words: what are the key levers that influence the outcome of the process you want to improve?

To get there, it’s helpful to ask a few focused questions: Which factors have the greatest impact on performance? In which magnitude do they occur? Can an AI solution influence them? And most importantly – where could AI truly add value?

Strong value drivers usually share three characteristics: they occur in large volumes, they significantly impact either costs or revenue, and they themselves can be significantly influenced by AI. If all three apply, you’re onto a high-potential value lever – and well on your way to building a solid business case.

A simplified example: A speciality chemicals manufacturer identifies the optimisation of maintenance for its complex, failure-prone machine park as a key strategic value lever. An in-depth analysis and discussions with plant management reveal one clear value driver in particular: the number of maintenance needs detected early.

This seems intuitive, as the earlier potential defects are identified, the more targeted can technicians be deployed to prevent the defects. The result: fewer unplanned production breakdowns – and with them, a significant reduction in revenue losses caused by downtime.

Step 2: Relate – Setting up the Equation

Once the key value drivers of a business process have been identified, the next step is to link these with an AI solution in a meaningful way. To do this, each driver is scrutinized in detail - this requires both creativity and close collaboration between specialist and tech expertise:

What specific output from an AI solution (e.g. an automatically generated error report or an intelligent product recommendation) really influences the respective value driver? And what type of AI solution would be best suited to deliver precisely this output?

These considerations are crucial for making the potential impact of an AI solution both realistic and tangible — and they serve as the first real litmus test for any AI initiative. They reveal whether the project may truly be worth pursuing — or better shelved.

In the example of the specialty chemicals manufacturer, AI solution, value drivers and financial outcomes were linked via the following value hypothesis: New machines could be equipped with heat and vibration sensors. An AI Agent - a sort of virtual maintenance manager working around the clock - could then automatically recognize anomalies in the sensor data, derive maintenance requirements and directly trigger a maintenance order to a technician. This would prevent more impending breakdowns.

The result would be fewer production interruptions, less foregone revenue - and therefore a measurable contribution to business success.

This also provides the basic arithmetic framework for the business case: avoided revenue loss = (avoided revenue loss per recognized maintenance need) × (number of recognized maintenance needs). Beyond this simplified form, further earnings effects were included (e.g. reduced emergency maintenance costs, reduced waste, reduced depreciation from total machinery losses).

Step 3: Estimate – Filling in the Numbers

Once the basic framework of the value equation is in place, the third step is to put numbers on the so far only qualitatively formulated equation - in other words, to quantify the expected added value.

This step involves taking a close look at each value driver, following the principle: “Facts first.” Hard data takes priority — such as measured values like process costs, processing times, or insights from previous feasibility studies. Where hard facts are not available, well-grounded expert estimates or experiences from similar projects can fill the gap.

This step is essential to make the added value of an AI project tangible for the first time - in figures that can be verified and communicated convincingly.

Back to the example of the chemical company: for the planned AI maintenance Agent, the previously defined value drivers were quantified based on empirical values from similar projects and estimations by the maintenance team.

The underlying assumption: the "AI maintenance manager" could boost early detection of maintenance needs by 16–22%. At present, downtime accounted for 4% of total production time, resulting in annual revenue losses of approximately €17.04 million.

In the most realistic scenario (‘middle case’), the team assumed that an increase of 19% in early recognized maintenance needs would reduce downtime to 3.24% - which would reduce the loss of revenue to around €13.81 million.

The result: the AI Agent would secure around €3.23 million in additional annual revenue through avoided production downtime alone - and thus deliver a clearly quantifiable added value.

Step 4: Substantiate – Making the Equation Waterproof

The final step is to substantiate all previous figures, assumptions and estimates and make them defendable. This is done by critically reviewing each variable in the value equation by technical experts, as well as cross-comparisons with the results of similar projects. Experiences values are critical here. This step is decisive for establishing credibility for the business case through transparency about data sources and assumptions, and to make the case justifiable to investment approval committees. Experience has shown that validation by a mixed team of internal and external experts creates the greatest confidence.

In the chemical company example, every figure and variable was backed by clear data sources, assumptions, and scenario analyses — all with the goal of strengthening the credibility and robustness of the value equation. The key assumptions here were that similar improvements could be achieved as in comparative projects, that all recognized maintenance requirements could translate into timely and successful maintenance, and that the AI maintenance Agent would not generate any false positives (i.e. erroneous maintenance orders). It was crucial here to clearly document the impact of each individual assumption on the overall added value. This way, if one assumption is challenged—such as during a business case discussion—it’s possible to pinpoint exactly how much that impacts the outcome. The key advantage: the overall value of the AI project isn’t immediately put into doubt. Instead, uncertainties can be assessed in a targeted, transparent way and evaluated separately.

Conclusion: Unlocking AI’s Value with the Value Equation

Our experience shows: In practice, simplicity and clarity are particularly convincing when it comes to communicating the added value of AI. The four steps of our ‘Value Equation’ were developed with precisely this guiding principle in mind.

Of course, there are more complex — and probably more precise — methods out there. But the four steps outlined above are essential. There’s no way around them in any serious AI business case.

What matters in practice is a balanced, pragmatic approach to implementation: Where can reliable estimates and experience values be sourced? What role does the project’s time horizon play? How should implementation costs be factored into the value equation? And just as importantly: how can the projected value be strategically framed and positioned within the internal political landscape? Answering these questions is key to turning a good idea into a convincing case for investment. They reveal why every business case requires careful, individual consideration. This is the only way to create robust, convincing value propositions - and thus a real basis for making AI investment decisions.